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on Computational Economics |
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Issue of 2018‒01‒01
five papers chosen by |
| By: | Potnuru Kishen Suraj; Ankesh Gupta; Makkunda Sharma; Sourabh Bikash Paul; Subhashis Banerjee |
| Abstract: | We develop a predictive machine learning based tool for accurate regression of development and socio-economic indicators from high resolution daytime satellite imagery. The indicators are derived from Census 2011 [The Ministry of Home Affairs, Government of India, 2011] and NFHS-4 [The Ministry of Health and Family Welfare, Government of India, 2016] survey data. We use a deep convolutional neural network to build a model for accurate prediction of asset indicators from satellite images. We show that the direct regression of asset indicators is more accurate than transfer learning through night light data, which is a popular proxy for economic development used world wide. We use the asset prediction model for accurate transfer learning of other socio-economic and health indicators which are not intuitively related to observable features in satellite images. The tool can be extended to monitor the progress of development of a region over time, and to flag potential anomalies because of dissimilar outcomes due to different policy interventions in a geographic region by detecting sharp spatial discontinuities in the regression output. |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1712.02282&r=cmp |
| By: | Joaquin Iglesias; Alvaro Ortiz; Tomasa Rodrigo |
| Abstract: | We apply the natural language processing or computational linguistics (NLP) to the analysis of the communication policy (i.e statements and minutes) of the Central Bank of Turkey (CBRT). While previous literature has focused on Developed countries, we extend the NLP analysis to the Central Banks of the Emerging Markets using the Dynamic Topic Modelling approach. |
| Keywords: | Working Paper , Central Banks , Digital economy , Economic Analysis , Emerging Economies , Turkey |
| JEL: | E52 E58 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:bbv:wpaper:1724&r=cmp |
| By: | Igor V. Kravchenko; Vladislav V. Kravchenko; Sergii M. Torba; Jos\'e Carlos Dias |
| Abstract: | This paper develops a novel analytically tractable Neumann series of Bessel functions representation for pricing (and hedging) European-style double barrier knock-out options, which can be applied to the whole class of one-dimensional time-homogeneous diffusions even for the cases where the corresponding transition density is not known. The proposed numerical method is shown to be efficient and simple to implement. To illustrate the flexibility and computational power of the algorithm, we develop an extended jump to default model that is able to capture several empirical regularities commonly observed in the literature. |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:1712.08247&r=cmp |
| By: | Chen, C.; Dollevoet, T.A.B.; Zhao, J. |
| Abstract: | We investigate the one-block train formation problem (TFP) in the railway freight transportation industry given a car route for each shipment. The TFP considers both the block design and the car-to-block assignment in the tactical level. Moving beyond current researches on service network design, the unitary rule and the intree rule are taken into account in this study based on the Chinese railway background. We develop a linear binary programming formulation to minimize the sum of train cost and classication delay subject to limitations on the classication capacity and the number of sort tracks at each station. Furthermore, we propose a novel solution methodology that applies a tree-based decomposition algorithm. Here, we rst decompose the whole network into a series of rooted trees for each destination separately. Then, we divide the trees into suciently small subtrees, whose size is regulated by a node size parameter. Finally, we construct a restricted linear binary model for each subtree and solve these models sequentially to nd their optimal solutions. Our computational results on a realistic network from the Chinese railway system with 83 stations, 158 links and 5700 randomly generated demands show that the proposed algorithm can derive high-quality solutions within 3 hours. These solutions are on average 43.89% better than those obtained after solving the linear binary program for 1 day. |
| Keywords: | Railway Freight Transportation, Train Formation Problem, Service Network Design, Tree-based Decomposition, Arborescence Structure |
| Date: | 2017–12–01 |
| URL: | http://d.repec.org/n?u=RePEc:ems:eureir:103193&r=cmp |
| By: | Xiao Xiao; Chen Zhou |
| Abstract: | This paper investigates the maximum entropy method for estimating the option implied volatility, skewness and kurtosis.The maximum entropy method allows for non-parametric estimation of the risk neutral distribution and construction of confidence intervals around the implied volatility. Numerical study shows that the maximum entropy method outperforms the existing methods such as the Black-Scholes model and model-free method when the underlying risk neutral distribution exhibits heavy tail and skewness. By applying this method to the S&P 500 index options, we find that the entropy-based implied volatility outperforms the Black-Scholes implied volatility and model-free implied volatility, in terms of in-sample fit and out-of-sample predictive power. The differences between entropy based and model-free implied moments can be explained by the level of the higher-order implied moments of the underlying distribution. |
| Keywords: | Option pricing; risk neutral distribution; higher order moments |
| JEL: | C14 G13 G17 |
| Date: | 2017–12 |
| URL: | http://d.repec.org/n?u=RePEc:dnb:dnbwpp:581&r=cmp |